CRISP uses the observed stability of positive voxel probability rankings under domain shift to build and iteratively refine high-precision and high-recall priors via latent feature perturbation, enabling parameter-free robust segmentation.
InProceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies (NAACL-HLT), pages 681–691
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A framework to identify and convert foldable layer normalizations to RMSNorm for exact equivalence and faster inference in deep neural networks.
Lens adapts camera sensors in real time via the VisiT confidence-based quality indicator to improve vision model accuracy on domain-shifted images, shown on ImageNet-ES and a new diverse benchmark.
Diversity-aware memory policies improve test-time adaptation performance most under constrained memory budgets and challenging non-i.i.d. streams.
TAME uses a Mixture-of-Experts prompt bank with input-dependent routing and three unsupervised objectives to adaptively defend CLIP against adversarial attacks at inference time, achieving at least 49.1% robustness gain on 11 datasets.
Shortcut Guardrail mitigates token-level shortcuts in pretrained language models at deployment time via gradient-based token identification and a LoRA-trained Masked Contrastive Learning module, improving accuracy under distribution shifts while preserving in-distribution performance.
Test-time entropy minimization adapts models by optimizing for confident predictions, reducing error on corrupted ImageNet-C and enabling source-free domain adaptation.
Threshold Modulation dynamically adjusts firing thresholds in SNNs via neuronal dynamics-inspired normalization to enable online test-time adaptation under distribution shifts.
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CRISP: Rank-Guided Iterative Squeezing for Robust Medical Image Segmentation under Domain Shift
CRISP uses the observed stability of positive voxel probability rankings under domain shift to build and iteratively refine high-precision and high-recall priors via latent feature perturbation, enabling parameter-free robust segmentation.
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Enjoy Your Layer Normalization with the Computational Efficiency of RMSNorm
A framework to identify and convert foldable layer normalizations to RMSNorm for exact equivalence and faster inference in deep neural networks.
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Adaptive Camera Sensor for Vision Models
Lens adapts camera sensors in real time via the VisiT confidence-based quality indicator to improve vision model accuracy on domain-shifted images, shown on ImageNet-ES and a new diverse benchmark.
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GoTTA be Diverse: Rethinking Memory Policies for Test-Time Adaptation
Diversity-aware memory policies improve test-time adaptation performance most under constrained memory budgets and challenging non-i.i.d. streams.
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TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models
TAME uses a Mixture-of-Experts prompt bank with input-dependent routing and three unsupervised objectives to adaptively defend CLIP against adversarial attacks at inference time, achieving at least 49.1% robustness gain on 11 datasets.
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Models Know Their Shortcuts: Deployment-Time Shortcut Mitigation
Shortcut Guardrail mitigates token-level shortcuts in pretrained language models at deployment time via gradient-based token identification and a LoRA-trained Masked Contrastive Learning module, improving accuracy under distribution shifts while preserving in-distribution performance.
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Tent: Fully Test-time Adaptation by Entropy Minimization
Test-time entropy minimization adapts models by optimizing for confident predictions, reducing error on corrupted ImageNet-C and enabling source-free domain adaptation.
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Threshold Modulation for Online Test-Time Adaptation of Spiking Neural Networks
Threshold Modulation dynamically adjusts firing thresholds in SNNs via neuronal dynamics-inspired normalization to enable online test-time adaptation under distribution shifts.